Fingerprint sensing apparatus and fingerprint identification method

ABSTRACT

A fingerprint sensing apparatus and a fingerprint identification method are provided. An original fingerprint image is obtained by the fingerprint sensor. An image edge block located at an edge of the original fingerprint image is selected. The image edge block is input into a neural network model to generate a predicted extension block. An extended fingerprint image is generated through merging the original fingerprint image with the predicted extension block. A fingerprint application is executed according to the extended fingerprint image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefits of U.S. provisionalapplication Ser. No. 63/020,030, filed on May 5, 2020; U.S. provisionalapplication Ser. No. 63/029,729, filed on May 26, 2020; and Chinaapplication serial no. 202110191961.5, filed on Feb. 19, 2021. Theentirety of each of the above-mentioned patent applications is herebyincorporated by reference herein and made a part of this specification.

BACKGROUND Technical Field

The disclosure relates to fingerprint identification technology, andparticularly, to a fingerprint sensing apparatus and a fingerprintidentification method.

Description of Related Art

In recent years, fingerprint identification technology has been widelyused either alone or in combination with various electronic devices orproducts to provide identity verification, and different fingerprintidentification technology, such as at least capacitive, optical, orultrasonic fingerprint sensing is being continuously developed andimproved. It is known that the size of a fingerprint image generated bya fingerprint sensor is relatively small when the sensing distance ofthe fingerprint sensor is limited due to various considerations. Whenfingerprint identification is performed according to local fingerprintinformation provided by a small area fingerprint image, a possiblemisjudgment may occur due to insufficient fingerprint features orbecause the fingerprints of different people are partially similar. Itis known that a fingerprint image can provide more fingerprint featureswhen the fingerprint image is in a larger size. Moreover, when multiplesmall area fingerprint images generated by multiple presses are stitchedaccording to the local fingerprint information provided by the smallarea fingerprint images, there may not be similar image blockscorresponding to the same fingerprint part among the small areafingerprint images, so the image stitching fails.

Therefore, some solutions have been proposed to solve the problemscaused by the small area fingerprint image. For example, directlyenlarging a small area fingerprint image and then performing fingerprintverification is adopted to increase the size of the fingerprint image totry to increase the quantity of the feature points, but in fact thismethod does not increase the amount of fingerprint information in thefingerprint image. In addition, stitching multiple small areafingerprint images into a more complete fingerprint image is alsoproposed. However, as mentioned, when the sensing distance of afingerprint sensor is relatively small, even if the user presses thefingerprint sensor multiple times, there may not be similar image blockscorresponding to the same fingerprint part among small area fingerprintimages. As a result, the image stitching fails.

SUMMARY

In view of this, the disclosure provides a fingerprint sensing apparatusand a fingerprint identification method capable of improving theaccuracy and the success rate of fingerprint matching in fingerprintidentification.

The embodiments of the disclosure provide a fingerprint sensingapparatus including a fingerprint sensor, a storage device, and aprocessor. The fingerprint sensor generates an original fingerprintimage. The processor is coupled to the fingerprint sensing apparatus andthe storage device and configured to execute the following steps. Animage edge block located at an edge of the original fingerprint image isselected. The image edge block is input into a neural network model togenerate a predicted extension block. An extended fingerprint image isgenerated through merging the original fingerprint image with thepredicted extension block. A fingerprint application is executedaccording to the extended fingerprint image.

In the embodiments of the disclosure, a fingerprint identificationmethod is provided and adapted for a fingerprint sensing apparatus. Themethod includes the following steps. An original fingerprint image isobtained by the fingerprint sensor. An image edge block located at anedge of the original fingerprint image is selected. The image edge blockis input into a neural network model to generate a predicted extensionblock. An extended fingerprint image is generated through merging theoriginal fingerprint image with the predicted extension block. Afingerprint application is executed according to the extendedfingerprint image.

Based on the above, in the embodiments of the disclosure, the neuralnetwork model is trained to generate predicted extension blocksaccording to the image edge blocks of the original fingerprint image.The predicted extension block may be merged with the originalfingerprint image to generate an extended fingerprint image. Theextended fingerprint image has more fingerprint features than theoriginal fingerprint image. Accordingly, the success rate of fingerprintmatching and the accuracy in fingerprint identification are improved.

In order to make the aforementioned features and advantages of thedisclosure comprehensible, embodiments accompanied with drawings aredescribed in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block view of a fingerprint sensing apparatus according toan embodiment of the disclosure.

FIG. 2 is a flowchart of a fingerprint identification method accordingto an embodiment of the disclosure.

FIG. 3A is a schematic view of a fingerprint identification methodaccording to an embodiment of the disclosure.

FIG. 3B is a schematic view of a fingerprint identification methodaccording to an embodiment of the disclosure.

FIG. 4 is a schematic view illustrating a generation of a predictedextension block according to an embodiment of the disclosure.

FIG. 5 is a schematic view illustrating generations of predictedextension blocks according to an embodiment of the disclosure.

FIG. 6A is a schematic view of adopting a neural network model togenerate predicted extension pixels of a predicted extension blockaccording to an embodiment of the disclosure.

FIG. 6B is a schematic view of adopting a neural network model togenerate predicted extension pixels of a predicted extension blockaccording to an embodiment of the disclosure.

FIG. 7 is a schematic view of generating an extended fingerprint imageaccording to an embodiment of the disclosure.

DESCRIPTION OF THE EMBODIMENTS

In order to make the content of the disclosure easier to understand, thefollowing specific embodiments are illustrated as examples of the actualimplementation of the disclosure. Moreover, whenever possible, the samereference numerals are used to represent the same or similar parts inthe accompanying drawings and description.

It should be understood that when an element is indicated to be“directly on another element” or “directly connected to” anotherelement, an element in the middle does not exist. For example, “toconnect” indicated in the specification may indicate to physicallyand/or electrically connect. Furthermore, “to electrically connect” or“coupled to” may also be used when other elements exist between twoelements.

Referring to FIG. 1, FIG. 1 is a block view of a fingerprint sensingapparatus according to an embodiment of the disclosure. The fingerprintidentification device 100 may include a fingerprint sensor 110, astorage device 120, and a processor 130. For example, the fingerprintsensing apparatus 100 is a notebook computer, a smart phone, a panel, agame console, other electronic devices/electrical equipment with thefunction of fingerprint identification, or the like, and the disclosureis not limited thereto.

The processor 130 may be coupled to the storage device 120 and thefingerprint sensor 110. The processor 130 may include a centralprocessing unit (CPU), an application processor (AP), other programmablegeneral-purpose or special-purpose microprocessors, a digital signalprocessor (DSP), a programmable controller, an application specificintegrated circuit (ASIC), a programmable logic device (PLD), or acombination thereof. In other words, the processor 130 may beimplemented by one or more integrated circuits (ICs), and the disclosureis not limited thereto. For example, the processor 130 may include anapplication processor and a sensing driver IC.

The storage device 120 is adopted to store data, software modules, andprogram codes. For example, the storage device 120 may be any type offixed or removable random access memory (RAM), read-only memory (ROM),flash memory, a hard disk or other similar devices, integrated circuits,and a combination thereof. In one embodiment, the processor 130 may loada program code or a module recorded in the storage device 120 to performthe fingerprint identification method proposed in the embodiments of thedisclosure.

The fingerprint sensor 110 may perform fingerprint sensing to generatean original fingerprint image. The disclosure does not limit the sensingmethod of the fingerprint sensor 110. The fingerprint sensor 110 may bean optical fingerprint sensor, an ultrasonic fingerprint sensor, or acapacitive fingerprint sensor. In one embodiment, the fingerprint sensor110 may have a smaller fingerprint sensing distance, and a small areaoriginal fingerprint image is generated according to the localfingerprint.

In one embodiment, the processor 130 may adopt a trained neural networkmodel to accurately predict fingerprint information not sensed by thefingerprint sensor 110 and merge the prediction result with the originalfingerprint image to generate an extended fingerprint image. Theextended fingerprint image may be adopted in the subsequent fingerprintapplications. In this way, the success rate of fingerprint matching ofthe small area original fingerprint image may be improved, and theproblem of failing to stitch images may also be improved.

FIG. 2 is a flowchart of a fingerprint identification method accordingto an embodiment of the disclosure. FIG. 3A is a schematic view of afingerprint identification method according to an embodiment of thedisclosure. Referring to FIG. 1, FIG. 2, and FIG. 3A, the method of theembodiment is adapted for the fingerprint sensing apparatus 100 in theforegoing embodiment, and in the following paragraph, the detailed stepsof the method in the embodiment are described accompanying with eachcomponent in the fingerprint sensing apparatus 100 and the embodiment ofFIG. 3A.

In step S210, the original fingerprint image is obtained by thefingerprint sensor 110. When the user places a finger on the fingerprintsensor 110, the fingerprint sensor 110 may generate an originalfingerprint image Img_ori according to the sensing data output by eachsensing unit in the fingerprint sensor 110. In the embodiment of FIG.3A, the fingerprint sensor 110 may generate the original fingerprintimage Img_ori of the size N*P. That is, the original fingerprint imageImg_ori includes N*P pixels.

In step S220, the processor 130 selects an image edge block located atan edge of the original fingerprint image. Meanwhile, the image edgeblock is an image block extending inward from a certain edge of theoriginal fingerprint image. The disclosure does not limit the size ofthe image edge block, which may be configured according to actualrequirements. FIG. 3A is an embodiment illustrating that the processor130 selects an image edge block EB1 located at the lower edge of theoriginal fingerprint image Img_ori, but the processor 130 may selectimage edge blocks located at any edge (e.g., the upper edge, the loweredge, the left edge, or the right edge) of the original fingerprintimage Img_ori, which is not limited in the disclosure. In the embodimentof FIG. 3A, the size of the image edge block EB1 is N*Q, where Q is lessthan P. That is, the image edge block EB1 includes N*Q pixels.

In step S230, the processor 130 inputs the image edge blocks to theneural network model to generate predicted extension blocks. That is,the neural network model may generate predicted extension blocks outputby the model according to image edge blocks input into the model. In oneembodiment, the neural network model is trained to predict fingerprintinformation that has not been selected in reality. In one embodiment,the neural network model may be a convolutional neural network (CNN)model. Meanwhile, the size of the predicted extension block output bythe model is less than the size of the image edge block input into themodel, but the actual size may be designed according to actualrequirements, and the disclosure is not limited thereto. In theembodiment of FIG. 3A, the processor 130 inputs the image edge block EB1to the neural network model to generate a predicted extension block PB1.The size of the predicted extension block PB1 is N*M, where M is lessthan Q. That is, the predicted extension block PB1 includes N*Mpredicted extension pixels.

In step S240, the processor 130 generates an extended fingerprint imageby merging the predicted extension block with the original fingerprintimage. Since the image edge block input into the model is located at acertain edge of the original fingerprint image, the processor 130 mayadhere the predicted extension block to the certain edge of the originalfingerprint image. In one embodiment, the predicted extension block mayinclude multiple predicted extension pixels. According to the edge wherethe image edge block is located, the processor 130 may merge thepredicted expansion pixels with the vertical edge or the horizontal edgeof the original fingerprint image, and the predicted expansion pixelsbecome pixels of the extended fingerprint image. In the embodiment ofFIG. 3A, after adhering the predicted extension block PB1 to the loweredge (i.e. the horizontal edge) of the original fingerprint imageImg_ori, the processor 130 may generate an extended fingerprint imageImg_e1 of the size N*(P+M).

Note that the neural network model is generated and trained according toauthentic fingerprint images, so it may accurately predict fingerprintinformation that is not sensed by the fingerprint sensor. Compared tosimply extending the fingerprint from the original fingerprint image,with the neural network model, the bifurcation, turning, spiral, orother special directions of the fingerprint may be predicted in a moreprecise manner in the embodiment of the disclosure.

In step S250, the processor 130 executes a fingerprint applicationaccording to the extended fingerprint image. The fingerprint applicationmay include a fingerprint registration process, a fingerprintverification process, or other related processes or programs requiringfingerprint images. In one embodiment, in a fingerprint verificationprocess, the processor 130 may perform the operation of capturingfingerprint feature points from the extended fingerprint image to obtainmultiple fingerprint feature points from the extended fingerprint image.Therefore, the processor 130 may determine whether the obtainedfingerprint feature points match the registration template featurepoints to obtain a fingerprint verification result. In one embodiment,in a fingerprint registration process, the processor 130 may perform theoperation of capturing fingerprint feature points from the extendedfingerprint image to obtain the registration template feature points andrecord them. In one embodiment, the user may press on the fingerprintsensor 110 with different parts of the fingers, and the processor 130may repeat steps S210 to S240 to generate multiple extended fingerprintimages. According to the overlapping areas among the multiple extendedfingerprint images, the processor 130 may perform image stitching on themultiple extended fingerprint images to generate a complete fingerprintimage. The complete fingerprint image may also be adopted in afingerprint verification process or a fingerprint registration process.

Note that FIG. 3A is an embodiment illustrating that the image edgeblock EB1 is selected from the horizontal edge of the originalfingerprint image Img_ori and the predicted extension block PB1 ismerged with the horizontal edge of the original fingerprint imageImg_ori, but the disclosure is not limited thereto. In one embodiment,the processor 130 may select the image edge block from the vertical edgeof the original fingerprint image and merge the corresponding predictedextension block with the vertical edge of the original fingerprintimage.

For example, FIG. 3B is a schematic view of a fingerprint identificationmethod according to an embodiment of the disclosure. The fingerprintsensor 110 may generate an original fingerprint image Img_ori of thesize N*P. The processor 130 selects an image edge block EB2 located atthe left edge of the original fingerprint image Img_ori. Meanwhile, thesize of the image edge block EB2 is Q*P, where Q is less than N. Thatis, the image edge block EB2 includes Q*P pixels. The processor 130 mayinput the image edge block EB2 to the neural network model, and theneural network model outputs a predicted extension block PB2. Meanwhile,the size of the predicted extension block PB2 is M*P, where M is lessthan Q. That is, the predicted extension block PB2 may include M*Ppredicted extension pixels. After merging the predicted extension blockPB2 onto the left edge of the original fingerprint image Img_ori, theprocessor 130 may generate an extended fingerprint image Img_e2 of thesize (N±M)*P.

Note that FIG. 3A and FIG. 3B are embodiments illustrating that theimage edge blocks EB1 and EB2 are selected from certain edges of theoriginal fingerprint image Img_ori and the corresponding predictedextension blocks PB1 and PB2 are generated, but the disclosure is notlimited thereto. Based on the same principle and process, the processor130 may select multiple image edge blocks from multiple edges of theoriginal fingerprint image and correspondingly generate multiplepredicted extension blocks.

In one embodiment, the processor 130 may input the image edge block tothe neural network model, and the neural network model may directlyoutput each prediction expansion pixel of the predicted extension block.That is, the processor 130 may directly predict a correspondingpredicted extension block according to an image edge block of theoriginal fingerprint image. For example, in the embodiments of FIG. 3Aand FIG. 3B, the neural network model may directly output the completepredicted extension blocks PB1 and PB2.

In one embodiment, the processor 130 may input the image edge blocks tothe neural network model, and the neural network model may outputpartial predicted extension block. Next, the processor 130 merges theimage edge block with the partial predicted extension block to generateanother image edge block and inputs the another image edge block to theneural network model, and the neural network model outputs anotherpartial predicted extension block. That is, the processor 130 may firstgenerate partial predicted expansion pixels of the predicted extensionblock through the neural network model according to an image edge blockof the original fingerprint image, and then the processor 130 generatesother partial predicted expansion pixels through the neural networkmodel again according to part of the image edge block and the partialpredicted extension block.

For example, FIG. 4 is a schematic view illustrating a generation of apredicted extension block according to an embodiment of the disclosure.Referring to FIG. 4, assuming that the fingerprint sensor 110 maygenerate the original fingerprint image Img_ori of the size N*P, theprocessor 130 may adopt the neural network model to generate a firstpartial block PB3_1 of the predicted extension block according to theimage edge block EB3 of the original fingerprint image Img_ori. Then,the processor 130 merges the image edge block EB3 with the first partialblock PB3_1 to generate another image edge block EB4. Meanwhile, theimage edge block EB4 includes part of the image edge block EB3 and thefirst partial block PB3_1. Next, the processor 130 inputs the anotherimage edge block EB4 to the neural network model, and the neural networkmodel outputs a second partial block PB3_2 of the predicted extensionblock to finally obtain the complete predicted extension block. Thus,the processor 130 may merge the first partial block PB3_1 and the secondpartial block PB3_2 of the predicted extension block in sequence withthe original fingerprint image Img_ori to generate the extendedfingerprint image Img_e3. Note that in FIG. 4, it is illustrated as anexample that the neural network model is adopted to make two predictionsto obtain the complete predicted extension block PB3, but the disclosureis not limited thereto. In other embodiments, based on similarprinciples and processes, the processor 130 may adopt the neural networkmodel to make more predictions to obtain a complete predicted extensionblock.

In one embodiment, the processor 130 may merge the predicted extensionblock with the original fingerprint image to generate a temporaryextended fingerprint image. The processor selects another image edgeblock located at an edge of the temporary extended fingerprint image.The processor inputs the another image edge block to the neural networkmodel to generate another predicted extension block. The processor 130merges the another predicted extension block with the temporary extendedfingerprint image to generate an extended fingerprint image. Meanwhile,the processor 130 merges the predicted extension block with an edge ofthe original fingerprint image in the first direction and merges theanother predicted extension block with an edge of the temporary extendedfingerprint image in the second direction. The edge in the firstdirection extends in the first direction, and the edge in the seconddirection extends in the second direction. The first direction isdifferent from the second direction. That is, the edge in the firstdirection and the edge in the second direction are a horizontal edge anda vertical edge, respectively.

In detail, when the processor 130 needs to add predicted extensionblocks to the left and right sides and the upper and lower sides of theoriginal fingerprint image, respectively, the processor 130 may predictthe predicted extension blocks on the left and right sides of theoriginal fingerprint image according to the partial expansion blocks onthe upper and lower sides of the predicted extension blocks and theimage edge blocks at the vertical edge of the original fingerprintimage. Alternatively, when the processor 130 needs to add predictedextension blocks to the left and right sides and the upper and lowersides of the original fingerprint image, respectively, the processor 130may predict the predicted extension blocks on the left and right sidesof the original fingerprint image according to the partial predictedextension blocks on the left and right sides of the predicted extensionblocks and the image edge blocks at the horizontal edge of the originalfingerprint image.

For example, FIG. 5 is a schematic view illustrating generations ofpredicted extension blocks according to an embodiment of the disclosure.Referring to FIG. 5, assuming that the fingerprint sensor 110 maygenerate the original fingerprint image Img_ori of the size N*P, theprocessor 130 may adopt the neural network model to generate a predictedextension block PB4 according to the image edge block EB5 of theoriginal fingerprint image Img_ori. Then, the processor 130 merges theoriginal fingerprint image Img_ori with the predicted extension blockPB4 to generate a temporary extended fingerprint image Img_te. Theprocessor 130 selects another image edge block EB6 located at an edge ofthe temporary extended fingerprint image Img_te. The processor inputsthe another image edge block EB6 to the neural network model to generateanother predicted extension block PB5. The processor 130 merges theanother predicted extension block PB5 with the temporary extendedfingerprint image Img_te to generate an extended fingerprint imageImg_e4. Note that in FIG. 5, it is illustrated as an example that theneural network model is adopted to first generate the predictedextension block at the horizontal edge and then the predicted extensionblock at the vertical edge, but the disclosure is not limited thereto.

In one embodiment, the neural network model includes multipleconvolution layers. The disclosure does not limit the quantity of theconvolution layers, which may be configured according to actualrequirements. Each of the convolution layers performs convolutionoperations according to one or more convolution kernels. The firstconvolution layer in the neural network model receives the image edgeblocks, and the last convolution layer in the neural network modelperforms convolution operations according to a convolution kernel tooutput part of or all of the predicted extension pixels of the predictedextension blocks. Note that, in one embodiment, the processor 130 mayadd padding blocks at opposite sides of the image edge block accordingto a preset input parameter and input the image edge blocks and thepadding blocks to the first convolution layer in the convolution layers.The size of the padding block is determined according to the presetinput parameter and the size of the image edge block. Meanwhile, thepreset input parameter of the neural network model is determinedaccording to the greater one between the image height and the imagewidth of the original fingerprint image. In one embodiment, the presetinput parameter of the neural network model includes the width of theinput image of the neural network model.

In detail, the convolution operation may cause the size of the outputdata of the convolution layers to be less than the size of the inputdata of the convolution layers. Therefore, in one embodiment, in orderto make the width of the output image of the neural network model equalto the quantity of pixel columns or the quantity of pixel rows of theoriginal fingerprint image or those of the temporary extendedfingerprint image, the processor 130 may add padding blocks at oppositesides of the image edge blocks. According to the operation of addingpadding blocks according to the preset input parameter, the preset inputparameter of the neural network model is determined according to thegreater one between the image height and image width of the originalfingerprint image, so the processor 130 may adopt a single neuralnetwork model to generate two predicted extension blocks in differentsizes for the vertical edge and the horizontal edge of the originalfingerprint image, respectively.

In the following paragraphs, the embodiments of adopting the neuralnetwork model to generate the predicted extension block are furtherillustrated.

FIG. 6A is a schematic view of adopting a neural network model togenerate predicted extension pixels of a predicted extension blockaccording to an embodiment of the disclosure. Referring to FIG. 6A, aneural network model 610 may include five convolution layers L1 to L5,and each of the convolution layers L1 to L5 performs convolutionoperations according to one or more convolution kernels. In theembodiment, it is illustrated as an example that the height of the inputimage of the neural network model 610 is 7 and the height of the outputimage of the neural network model 610, but the disclosure is not limitedthereto.

In the embodiment of FIG. 6A, assuming that the size of the originalfingerprint image is N*P and the image width N is greater than the imageheight P, the processor 130 may determine that the preset inputparameter of the neural network model 610 is equal to the sum of theimage width N plus a constant parameter (the constant parameter isexemplified by 4). That is, the preset input parameter of the neuralnetwork model 610 is configured to be equal to N+4. In one embodiment,the constant parameter may be determined according to the height of theinput image and the height of the output image of the neural networkmodel 610. For example, in the embodiment of FIG. 6A, since the heightof the input image of the neural network model 610 is 7 and the heightof the output image of the neural network model 610 is 3, to ensure thatthe width of the output image of the neural network model 610 is equalto the quantity of pixel columns or the quantity of pixel rows of theoriginal fingerprint image or those of the temporary extendedfingerprint image, the processor 130 may determine that the constantparameter may be an even number greater than or equal to 4.

In the embodiment, under the condition that the preset input parameterof the neural network model 610 is equal to N+4, when an image edgeblock EB7 of the size N*7 is input to the neural network model 610, theprocessor 130 may determine that the size of padding blocks ZP1 and ZP2which are adopted to be added at two sides of the image edge block EB7is 2*7. In addition, the model parameters of the neural network model610 may be as shown in Table 1, for example.

TABLE 1 the size of the quantity of the convolution the convolution zerooperation kernel kernels padding convolution convolution 3*3 4 samelayer L1 operation convolution convolution 3*3 8 valid layer L2operation convolution convolution 3*3 12 same layer L3 operationconvolution convolution 3*3 16 valid layer L4 operation convolutionconvolution 3*3 1 same layer L5 operationIn addition, each of the convolution layers L1 to L5 of the neuralnetwork model 610 shown in FIG. 6A and Table 1 performs convolutionoperations with a stride equal to 1 (stride=1), but the disclosure isnot limited thereto. However, the model parameters shown in Table 1 areonly exemplary descriptions to clearly illustrate the content of theembodiments of the disclosure rather than to limit the disclosure. Thesize of the convolution kernel, the quantity of the convolution kernels,the zero padding method, and the stride used in each of the convolutionlayers L1 to L5 of the neural network model may be configured accordingto actual requirements.

Note that the neural network model 610 may also be adopted to generatepredicted extension blocks for edge image blocks at the vertical edge.For example, FIG. 6B is a schematic view of adopting a neural networkmodel to generate predicted extension pixels of a predicted extensionblock according to an embodiment of the disclosure. Referring to FIG.6B, assuming that the size of the original fingerprint image is N*P andthe image width N is greater than the image height P, under thecondition that the preset input parameter of the neural network model610 is equal to N+4, when an image edge block EB8 of the size 7*P isinput to the neural network model 610, the processor 130 may firstrotate the image edge block EB8 of the size 7*P to generate an imageedge block EB8 of the size P*7. Then, the processor 130 may determinethat the size of padding blocks ZP3 and ZP4 to be added at two sides ofthe image edge block EB8 is (N+4-P)/2*7. After the convolution operationof the convolution layers L1 to L5, the processor 130 may select apredicted extension block PB6 of the size P*3 from the model output ofthe neural network model 610. The processor 130 may first rotate thepredicted extension block PB6 of the size P*3 to generate a predictedextension block PB6 of the size 3*P to merge the predicted extensionblock PB6 of the size 3*P with the vertical edge of the originalfingerprint image of the size N*P in the subsequent operations.According to FIG. 6A and FIG. 6B, in one embodiment, the same set of thetrained neural network model may be adopted to generate predictedextension blocks in different sizes.

Note that, in one embodiment, the predicted extension block output bythe neural network model may include a predicted fingerprint ridge or apredicted fingerprint valley. The predicted fingerprint ridge is notconnected to any fingerprint ridge or any fingerprint valley in theoriginal fingerprint image. That is, the predicted extension block mayinclude predicted fingerprint lines that do not extend from fingerprintridges or fingerprint valleys in the original fingerprint image. Indetail, the neural network model of the embodiments in the disclosuremay be trained and established according to various types of fingerprintorientations, so the neural network model of the embodiments in thedisclosure may predict the bifurcation, turning, spiral, or otherspecial directions of the fingerprint more accurately instead of simplyextending the fingerprint ridges or fingerprint valleys in thefingerprint image.

FIG. 7 is a schematic view of generating an extended fingerprint imageaccording to an embodiment of the disclosure. Referring to FIG. 7, theoriginal fingerprint image Img_ori is a small area fingerprint imagewith spiral fingerprints. In the embodiment, two image edge blockslocated at the upper edge and the lower edge of the original fingerprintimage Img_ori are respectively selected as the input data of the neuralnetwork model. The neural network model may output two correspondingpredicted extension blocks PB7 and PB8, respectively. An extendedfingerprint image Img_e5 may be generated by merging the predictedextension blocks PB7 and PB8 with the original fingerprint imageImg_ori. In the embodiment of FIG. 7, the neural network model mayaccurately predict the spiral direction of the fingerprint to generatethe extended fingerprint image Img_e5 with high reliability. Morespecifically, the predicted extension block PB8 includes predictedfingerprint lines that do not extend from fingerprint ridges andfingerprint valleys in the original fingerprint image Img_ori.

Based on the above, in the embodiments of the disclosure, the neuralnetwork model is trained to generate a predicted extension blockaccording to the image edge blocks of the original fingerprint image.The predicted extension block may be merged with the originalfingerprint image to generate an extended fingerprint image. Theextended fingerprint image has more fingerprint features than theoriginal fingerprint image. In this way, the success rate of fingerprintmatching and the accuracy in fingerprint identification may be improved,and the problem of failing to smoothly stitch multiple fingerprintimages may also be prevented. In addition, the neural network model istrained and established according to authentic fingerprint images, so itmay accurately predict fingerprint information that is not sensed by thefingerprint sensor, ensuring the extended fingerprint image to havereliability to a certain degree.

It should be finally noted that the above embodiments are merelyintended for describing the technical solutions of the presentdisclosure rather than limiting the present disclosure. Although thepresent disclosure is described in detail with reference to theforegoing embodiments, those of ordinary skill in the art shouldunderstand that they can still make modifications to the technicalsolutions described in the foregoing embodiments or make equivalentsubstitutions to some or all technical features thereof, withoutdeparting from scope of the technical solutions of the embodiments ofthe present disclosure.

What is claimed is:
 1. A fingerprint sensing apparatus, comprising: a fingerprint sensor generating an original fingerprint image; a storage device; and a processor coupled to the fingerprint sensor and the storage device and configured to: select an image edge block located at an edge of the original fingerprint image; input the image edge block into a neural network model to generate a predicted extension block; generate an extended fingerprint image through merging the original fingerprint image with the predicted extension block; and execute a fingerprint application according to the extended fingerprint image.
 2. The fingerprint sensing apparatus according to claim 1, wherein the predicted extension block comprises a plurality of predicted extension pixels, the processor inputs the image edge block to the neural network model, and the neural network model outputs each of the predicted extension pixels of the predicted extension block.
 3. The fingerprint sensing apparatus according to claim 1, wherein the processor inputs the image edge block to the neural network model, the predicted extension block outputs part of the predicted extension block, and the processor merges the image edge block with part of the predicted extension block to generate another image edge block and input the another image edge block to the neural network model, so the neural network model outputs another part of the predicted extension block.
 4. The fingerprint sensing apparatus according to claim 1, wherein the predicted extension block comprises a plurality of predicted extension pixels, and the processor merges the plurality of predicted extension pixels with a vertical edge or a horizontal edge of the original fingerprint image.
 5. The fingerprint sensing apparatus according to claim 1, wherein the processor merges the predicted extension block with the original fingerprint image to generate a temporary extended fingerprint image, the processor selects another image edge block located at an edge of the temporary extended fingerprint image, the processor inputs the another image edge block to the neural network model to generate another predicted extension block, and the processor merges the another predicted extension block with the temporary extended fingerprint image to generate the extended fingerprint image.
 6. The fingerprint sensing apparatus according to claim 1, wherein the fingerprint application comprises a fingerprint registration process or a fingerprint verification process.
 7. The fingerprint sensing apparatus according to claim 1, wherein the neural network model comprises a plurality of convolution layers, the processor adds padding blocks on opposite sides of the image edge block according to a preset input parameter and inputs the image edge block and the padding blocks to a first convolution layer of the plurality of convolution layers, sizes of the padding blocks are determined according to the preset input parameter and a size of the image edge block, and the preset input parameter is determined according to a greater one between on an image height and an image width of the original fingerprint image.
 8. The fingerprint sensing apparatus according to claim 1, wherein the predicted extension block comprises a predicted fingerprint ridge or a predicted fingerprint valley, the predicted fingerprint ridge is not connected to any fingerprint ridge in the original fingerprint image, and the predicted fingerprint valley is not connected to any fingerprint valley in the original fingerprint.
 9. A fingerprint identification method, adapted for a fingerprint sensing apparatus, the method comprising: obtaining an original fingerprint image by the fingerprint sensor; capturing an image edge block located at an edge of the original fingerprint image; inputting the image edge block to a neural network model to generate a predicted extension block; generating an extended fingerprint image by merging the original fingerprint image with the predicted extension block; and executing a fingerprint application according to the extended fingerprint image.
 10. The fingerprint identification method according to claim 9, wherein the predicted extension block comprises a plurality of predicted extension pixels, and the step of inputting the image edge block to the neural network model to generate the predicted extension block comprises: inputting the image edge block to the neural network model so that the neural network model outputs each of the plurality of predicted extension pixels of the predicted extension block.
 11. The fingerprint identification method according to claim 9, wherein the step of inputting the image edge block to the neural network model to generate the predicted extension block comprises: inputting the image edge block to the neural network model so that the neural network model outputs part of the predicted extension block; merging the image edge block with the part of the predicted extension block to generate another image edge block; and inputting the another image edge block to the neural network model so that the neural network model outputs another part of the predicted extension block.
 12. The fingerprint identification method according to claim 9, wherein the predicted extension block comprises a plurality of predicted extension pixels, and the step of generating the extended fingerprint image by merging the original fingerprint image with the predicted extension block comprises: merging the plurality of predicted extension pixels with a vertical edge or a horizontal edge of the original fingerprint image.
 13. The fingerprint identification method according to claim 9, wherein the step of generating the extended fingerprint image by merging the original fingerprint image with the predicted extension block comprises: merging the predicted extension block with the original fingerprint image to generate a temporary extended fingerprint image; capturing another image edge block located at an edge of the temporary extended fingerprint image; inputting the another image edge block to the neural network model to generate another predicted extension block; and merging the another predicted extension block with the temporary extended fingerprint image to generate the extended fingerprint image.
 14. The fingerprint identification method according to claim 9, wherein the fingerprint application comprises a fingerprint registration process or a fingerprint verification process.
 15. The fingerprint identification method according to claim 9, wherein the neural network model comprises a plurality of convolution layers, the processor adds padding blocks on opposite sides of the image edge block according to a preset input parameter and inputs the image edge block and the padding blocks to a first convolution layer of the plurality of convolution layers, sizes of the padding blocks are determined according to the preset input parameter and a size of the image edge block, and the preset input parameter is determined according to a greater one between on an image height and an image width of the original fingerprint image.
 16. The fingerprint identification method according to claim 9, wherein the predicted extension block comprises a predicted fingerprint ridge or a predicted fingerprint valley, the predicted fingerprint ridge is not connected to any fingerprint ridge in the original fingerprint image, and the predicted fingerprint valley is not connected to any fingerprint valley in the original fingerprint. 